Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits - Guo, Xinjie; Merrikh-Bayat, Farnood; Gao, Ligang; Hoskins, Brian D.; Alibart, Fabien; Linares-Barranco, Bernabe; Theogarajan, Luke; Teuscher, Christof; Strukov, Dmitri B. (Frontiers in Neuroscience, 2015)
The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC’s precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2-x/Pt memristors and CMOS integrated circuit components.